One obvious application of time delay determination is the synchronisation of different processes or functions being performed in a complicated engineering system, especially a communication system. There are many other practical applications of time delay determination; for example, radar and sonar systems. Also, in some industrial and biomedical applications, where a distance is known, but the velocity of a waveform, associated with some phenomenon or process, is required, this can be estimated by determining the time required for this phenomenon or process to travel the known distance.
One conventional method of determining a time delay Δt between two signals x(t) and y(t) is to estimate the standard cross-correlation functionRxy(τ)=(1/T)∫x(t).y(t+τ)dt=(1/T)∫x(t−τ). y(t)dtwhere the integral is evaluated over the observation interval of duration T and for a range of hypothesised time delays τmin<τ<τmax. The value of argument τ, say τ0, that maximises the cross-correlation function Rxy(τ) provides an estimate of the unknown time delay Δt.
In general, the operation of cross-correlation comprises the following three steps:    1. delaying the reference signal x(t) by τ;    2. multiplying the values of a received signal y(t) and delayed reference x(t);    3. integrating the product obtained in step 2 over a specified observation time interval T.
A block diagram of a standard cross-correlator system is presented in FIG. 1. The system comprises a variable delay line 100, a multiplier 102 and an integrator 104. An example of a cross-correlation curve, with its maximum determining the time delay estimate τ0, is shown in FIG. 2.
WO-A-00/39643 discloses an improved technique for the calculation of the time delay between signals using a technique referred to herein as “crosslation”. The contents of WO-A-00/39643 are incorporated herein by reference.
The term “crosslation” as used herein refers to a technique whereby predefined (preferably at least substantially aperiodic) events which occur in one signal are used to define staggered segments of a second signal, and representations of the staggered segments are then combined. The first and second signals may in fact be the same signal, in which case the resulting combined representation will provide information regarding the statistical properties of that signal, and in particular about the average behaviour of the signal before and after the predefined events. Alternatively, the first and second signals may be different signals (“mutual crosslation”), or one may be a delayed version of the other, in which case the combined representation will provide information about the relationship between those signals. For example, if the combined representation contains a feature which would be expected from combining segments associated with multiple predefined events, this may indicate that one of the signals is delayed with respect to the other by an amount corresponding to the position within the representation of that feature.
According to WO-A-00/39643, a binary, bipolar signal is subjected to an unknown delay. The non-delayed version of the signal is examined to determine when its level crosses zero with a positive slope (an upcrossing). The timing of these crossing events is used to obtain respective segments of the delayed signal, the segments having a predetermined duration. The segments are all summed, and a representation of the summed segments is then examined to locate a feature in the form of an odd function. The position within the representation of a zero-crossing in the centre of the odd function represents the amount by which the signal has been delayed. Instead of using upcrossings, the non-delayed version of the signal could be examined to determine when its level crosses zero with a negative slope (downcrossings).
WO-A-00/39643 also suggests improving accuracy by using both upcrossings and downcrossings. In this case, the segments defined by the downcrossings are subtracted from the segments defined by the upcrossings to obtain the odd function which is then examined.
The crosslation techniques of WO-A-00/39643 are particularly suited for object tracking with the use of active sensors, such as radar or active sonar, in which the surveillance region of interest is illuminated by an interrogating energy waveform to obtain object-backscattered returns. In such circumstances suitable (e.g., binary) signals can be chosen for modulating the transmitted signal. However, the technique can be less advantageous in applications involving passive sensors which capture only object-generated signals (or object-influenced signals from separate sources), for example systems for detecting, localising and tracking the movement of people, wheeled or tracked vehicles, speedboats or vibrating machinery, etc. using wideband acoustic signals generated thereby. It would also be desirable to provide a system which generates an output which is better suited for some applications than the odd function generated by the system of WO-A-00/39643 (e.g. applications which are primarily intended for object detection, rather than tracking of an already-detected object).
Object-generated acoustic signals are classified as wideband signals since the ratio of their highest frequency component to lowest frequency component is relatively large. For example, for the audio range, 30 Hz to 15 kHz, the ratio is 500. In a case of wheeled and tracked vehicles, dominant frequency components may range from about 20 Hz to 2 kHz, resulting in a ratio of 100.
Not only do acoustic signals emitted by objects of interest occupy a wide frequency range, but they also will manifest a non-stationary and chaotic nature with identifiable intermittent transients. As a result, many known cross-correlation techniques based, explicitly or implicitly, on the assumptions of signal stationarity and noise Gaussianity are only of limited practical use. Furthermore, most practical implementations have to deal with discrete-time samples, so that the optimisation procedures and performance analyses carried out in the continuous-time framework cannot be fully applicable.
A specific example of an application in which improved techniques for object detection and localisation would be desirable is that of security surveillance with a network of distributed acoustic sensors forming an ‘acoustic fence’. When an object of interest, such as a vehicle, has been detected and localised, the estimated object position can be utilized by security cameras for aiming and zooming in order to enhance the quality of recorded images. Such systems may be installed for monitoring purposes in industrial environments, e.g. to track moving objects, or to offer improved continuous surveillance of critical infrastructure, including power grids, power plants, gas and oil pipelines and water systems. Another application is that of coastguard or littoral surveillance in which speedboats and other surface vessels of interest can be detected and localised by a network of floating buoys employing acoustic sensors and low-power radio transceivers providing an intersensor communication link.
In addition to the above surveillance and reconnaissance applications, it would also be desirable to improve multimedia applications involving distributed microphone networks which are capable of enhancing audio signals for improved intelligibility, and cuing for camera aiming.
Accordingly, it would be desirable to provide an improved technique for time delay measurement, for example for use in object detection systems, including object locating and object tracking systems.